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PI: Richard D. Lange

I am an assistant professor in the Department of Computer Science at RIT, with additional appointments in the Cognitive Science PhD Program and the Center for Vision Science.

I want to understand how the brain works and use that understanding to create more capable machine learning and AI systems. My interests have bounced back and forth over the years between computer science, neuroscience, AI, machine learning, and philosophy of mind. 

Here's a bit about how I got where I am now:

New and ongoing research areas – get in touch if you're interested in any of the following!

*  I am actively recruiting new PhD students for Fall 2025 in these areas!

Some opportunities for MS and UG students to get involved in these areas

How to get involved

Current RIT MS or UG students

Reach out to talk about possible thesis or capstone projects. It's common and encouraged to do an independent study first so you can spend 1 semester learning background material and 1 semester doing your project.

Reach out early! My schedule fills up quickly.

Paid RA positions are not currently available, but might be in the future, pending funding.

Apply to do a PhD

Note: availability for 2025 start depends on grant funding, which is TBD.

Finding the right PhD program means not just finding a research area you're interested in, but also finding a program and an advisor you'll get along with. If you're interested, please apply to the RIT GCCIS PhD program or the Cognitive Science PhD program and mention my name in your application.

As is the case with many PhD programs at other institutions, funding for PhD students is guaranteed for the first year and contingent on external grant funding – either to the lab or to the student directly – going forward. While it is very unlikely that funding issues would arise, I feel it is important for prospective applicants to be aware of what goes on behind the scenes and the small but real possibility that funding issues do eventually arise.

Past research highlights

Click here for Google Scholar

Understanding neural representations

Vision as Bayesian inference

Improving inference algorithms

Inference dynamics during decision-making